A global optimization method for the design of space trajectories

Addis, Bernardetta; Cassioli, Andrea; Locatelli, Marco; Schoen, Fabio

doi:10.1007/s10589-009-9261-6

Cited by 109 publications

(43 citation statements)

References 23 publications

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“…A problem may have several such funnels. MBH was originally developed to solve molecular conformation problems in computational chemistry, but has been demonstrated to be effective on various types of interplanetary trajectory problems [16,17,40,35,18,36,41]. MBH is a two-stage solver that alternates back and forth between a stochastic global search stage and a deterministic NLP stage.…”

Section: A Stochastic Global Search Via Monotonic Basin Hopping and mentioning

confidence: 99%

Automated Solution of the Low-Thrust Interplanetary Trajectory Problem

Englander

Conway

2017

Journal of Guidance, Control, and Dynamics

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Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. In addition, a time-history of control variables must be chosen that defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated, which can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a hybrid optimal control problem. The method is demonstrated on hypothetical missions to Mercury, the main asteroid belt, and Pluto.

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Section: A Stochastic Global Search Via Monotonic Basin Hopping and mentioning

confidence: 99%

Automated Solution of the Low-Thrust Interplanetary Trajectory Problem

Englander

Conway

2017

Journal of Guidance, Control, and Dynamics

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“…This problem consists in finding suitable parameter values which are used to define a trajectory for a space vehicle. The literature on this subject is quite vast -we refer the readers, e.g., to (Addis, Cassioli, Locatelli, & Schoen, 2009;Izzo, Becerra, Myatt, Nasuto, & Bishop, 2007;Olympio & Marmorat, 2008;Vasile, 2005) for references on this subject. Here it may suffice to say that this is a family of very hard global optimization problems with relatively few variables (a few tens at most) but a huge amount of local optima.…”

Section: Space Trajectory Designmentioning

confidence: 99%

“…The first trials, starting from a database of 1 000 MBH runs performed as described in (Addis et al, 2009), were prepared using the same scheme as before, with a partition of the starting point set into 500 for training and 500 for validation. Unfortunately, with these data the first attempts to train an SVM typically lead to a failure, which consisted in having a SVM which refuses every point, at least for reasonable choices of the threshold used to distinguish acceptable from unacceptable points.…”

Section: Space Trajectory Designmentioning

confidence: 99%

Machine learning for global optimization

et al. 2010

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In this paper we introduce the LeGO (Learning for Global Optimization) approach for global optimization in which machine learning is used to predict the outcome of a computationally expensive global optimization run, based upon a suitable training performed by standard runs of the same global optimization method. We propose to use a Support Vector Machine (although different machine learning tools might be employed) to learn the relationship between the starting point of an algorithm and the final outcome (which is usually related to the function value at the point returned by the procedure). Numerical experiments performed both on classical test functions and on difficult space trajectory planning problems show that the proposed approach can be very effective in identifying good starting points for global optimization.

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“…First, the position of algorithm curr in the sorted list of algorithms rank is obtained through the lookup call. If the current algorithm is no longer eligible, we switch to the best algorithm available (that is, rank [1]). Otherwise, the algorithm one rank better than the current algorithm is chosen.…”

Section: Prunermentioning

confidence: 99%

“…The two problems have 6 and 22 real variables, respectively, and an unknown number of local optima. These problems have been extensively studied and are known to contain an enormous number of local optima and to be strongly deceptive for local optimizers [1].…”

Section: Test Functionsmentioning

confidence: 99%

Distributed hyper-heuristics for real parameter optimization

Biazzini

Bánhelyi

Montresor

et al. 2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance than any of the original heuristics. While HHs lend themselves naturally for distributed deployment, relatively little attention has been paid so far on the design and evaluation of distributed HHs. To our knowledge, our work is the first to present a detailed evaluation and comparison of distributed HHs for real parameter optimization in an island model. Our set of test functions includes well-known benchmark functions and two realistic space-probe trajectory optimization problems. The set of algorithms available to the HHs include several variants of differential evolution, and uniform random search. Our main conclusion is that some of the simplest HHs are surprisingly successful in a distributed environment, and the best HHs we tested provide a robust and stable good performance over a wide range of scenarios and parameters.

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A global optimization method for the design of space trajectories

Abstract: Global optimization, Space trajectories, Implicit filter, Black-box, Basin hopping,

Cited by 109 publications

References 23 publications

Automated Solution of the Low-Thrust Interplanetary Trajectory Problem

Automated Solution of the Low-Thrust Interplanetary Trajectory Problem

Machine learning for global optimization

Distributed hyper-heuristics for real parameter optimization

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